AI Context Window: Why Do Language Models Have Limited Memory?
Have you ever had a long, fascinating discussion with an AI chat, only to realize at some point that the model had completely forgotten what you were talking about at the very beginning? Or perhaps you tried to paste an entire book for analysis, only for the system to refuse to cooperate, claiming that the message was “too long”?
It is neither machine malice nor a software error. It is an encounter with one of the most fundamental limitations of modern artificial intelligence: the context window.
Although large language models (LLMs) have “read” nearly the entire internet during training, their working memory (which they use while conversing with you) is surprisingly limited and, more importantly, extremely computationally expensive. Why is giving AI “more memory” much harder than simply adding a RAM module to a laptop? Why does the mathematics behind text generation mean that every additional sentence in the chat history is a challenge for supercomputers?
In this article, we will look under the hood of language models. We will explain how tokens work, why the costs of “attention” grow exponentially, and how engineers (and users) address this limitation by using clever techniques such as RAG and sliding windows.
The article is written in a way that makes it useful for people who do not have much knowledge about artificial intelligence. However, understanding the fundamentals of how artificial intelligence works significantly facilitates understanding the concept of the context window.
Table of Contents
What is a context window?
This can be compared to talking to someone who can remember only the last ten sentences of a conversation. No matter how long you talk to each other, everything you said earlier simply disappears from their memory. This is exactly what happens with language models—their “working memory” is limited by what is known as the context window.
The context window is the maximum amount of text (measured in tokens) that a model can process at once. A token is usually part of a word, a whole word, or a punctuation mark; in Polish, on average, one word is approximately 1.5–2 tokens. When you exceed the context window limit, the model simply “forgets” the oldest parts, retaining only the most recent information.
In practice, this means that if a model has a 4,000-token context window (approximately 3,000 words in Polish) and you send it a 10,000-word document, it will see only the end. All the important information from the beginning of the document will be unavailable to it. It’s like trying to understand a film by watching only the last twenty minutes (or, more precisely, as if someone erased your memories of the previous 20 minutes every minute).
Why not simply increase the context window size?
At first glance, the solution seems obvious: simply expand the context window from 4,000 to 100,000 tokens and the problem is solved. However, reality is far more complex, and the limitations are fundamental.
Above all, the mathematics behind language models leaves no room for compromise. With quadratic computational complexity, increasing the window tenfold translates into a hundredfold increase in the required computations. A model that efficiently processed 4,000 tokens per second would need several minutes to process 40,000 tokens, while also requiring dramatically more GPU memory.
Another problem is structural in nature: models learn with a specific context size. Positional embeddings, mathematical markers indicating the order of tokens, are precisely calibrated for a particular maximum length. When we suddenly provide the model with a sequence far longer than the one it was trained on, its effectiveness drops sharply. It is like trying to read a map of Poland at a scale of 1:500,000 to find your way around Szklarska Poręba—the proportions simply do not match.
Finally, there is a quality issue: as the context length increases, the model loses its ability to process information evenly. Research has revealed a phenomenon known as “lost in the middle”; models tend to overlook passages in the middle of very long sequences, focusing their attention mainly on the beginning and the end. This is not merely a technical issue, but a limitation arising from the architecture itself.
The Mathematics Behind the Context Window
Tokens and Embeddings
Before the model can even begin “thinking” about the text, it must first translate it into the language of numbers. Each token is transformed into a vector, an ordered sequence of hundreds or thousands of floating-point numbers. We call this process embedding. For example, the word “cat” may take the form of a vector [0.234, -0.891, 0.445, …, 0.123] with 768 or more dimensions.
These vectors do not arise by chance—they are designed so that words with similar meanings have similar numerical representations. The generated embeddings are then placed into a matrix that becomes the foundation for all subsequent calculations in the model. If you process a sequence of 1,000 tokens, each with a 768-dimensional embedding, you are working with a matrix measuring 1,000 × 768. And this is only the starting point.
A multidimensional matrix is much easier to understand than most people think. In theory, models could use a three-dimensional or even two-dimensional coordinate system. Everyone can imagine such a thing, and it is even easy for a human to perform operations in such a system. Two coordinates are sufficient to describe any point, and describing any vector is also straightforward.
The three-dimensional coordinate system is also easy to understand; anyone can work with it. Only the description of a vector (and a point) changes slightly. Now, if we want to describe the position of any point, we must use three coordinates: length, width, and height.
And what does it look like in a four-dimensional coordinate system? Visualization is very difficult, practically impossible, although one can try to approximate the appearance of such a system in many ways, but it is no longer possible to work with it visually. On the other hand, describing any point or vector is still relatively simple. To describe any point, it is enough to add another coordinate. All vector addition and subtraction operations essentially work in the same way. In this way, systems with any number of coordinates can be created.
The Attention Mechanism and Its Cost
At the heart of every modern language model is a mechanism called attention. It enables the model to “scan” different parts of the text and decide which elements are crucial for understanding the word currently being analyzed. It sounds harmless—but this is precisely where the real challenge lies.
In the classic implementation of attention (called self-attention), the model compares each token with every other token in the sequence. Mathematically, this is achieved through matrix multiplication: we multiply the matrix containing all tokens by its transpose, obtaining the attention matrix. For a sequence of length N, this produces a matrix with dimensions N × N.
And this is precisely where the problem becomes apparent: with 1,000 tokens, the model must calculate 1,000,000 relationships (1,000 × 1,000). When you expand the context to 10,000 tokens, that number jumps to 100,000,000 relationships. Computational complexity grows quadratically with the sequence length, which we write as O(N²). This is not a linear increase—it is a true explosion.
Moreover, a typical model consists of many layers (often 24, 32, or more), each of which performs its own attention computations. Many models also use a dozen or more parallel attention “heads” in each layer, further multiplying the computational requirements. On top of all this, memory is needed to store these numerous matrices during computation.
Graphic: Dancing Philosopher
Practical Ways to Increase the Context Window
Chunking and summarization
The simplest method of circumventing context window limitations is to divide a long text into smaller fragments (chunks) and process them sequentially. For example, you can divide a 50-page document into 10 sections of 5 pages each, analyze each one separately, and then compile the results.
Even better is cascading summarization: you divide the document into segments, summarize each segment, and then create a “meta-summary” from all the summaries. It’s like reading meeting notes instead of full transcripts—you lose the details, but retain the essence. This method is particularly useful when you need a general understanding rather than verbatim quotations.
RAG (Retrieval-Augmented Generation)
RAG is a much more sophisticated approach. Instead of feeding the entire document into the model, the system first retrieves only the fragments that are relevant to your question. It works as follows: you have a document database converted into embeddings, you ask a question, the system finds the fragments that are most semantically similar (using vector similarity), and then only those fragments are sent to the model along with the question.
Suppose you have a thousand company reports and ask, “What were the marketing costs in Q3?” A RAG system doesn’t need to read all thousand documents—it will find the 3–5 excerpts that mention Q3 and marketing and send only those to the model. It’s like having a brilliant librarian who always knows which shelf holds what you’re looking for.
RAG works great with knowledge bases, technical documentation, and archives. However, it requires additional infrastructure: a vector database, an indexing system, and a search mechanism. Tools such as LangChain and LlamaIndex make this much easier.
Sliding window and Other Optimizations
Some newer architectures use a sliding window: instead of looking at all tokens at once, each token sees only its N nearest neighbors. The Mistral 7B model uses exactly this approach with a 4,096-token window, but thanks to its clever architecture, it can efficiently process much longer sequences.
Another technique is sparse attention—instead of calculating relationships between all pairs of tokens, the model focuses only on selected, strategically important relationships. This reduces complexity from O(N²) to O(N log N) or even O(N). Models such as Longformer and BigBird use these optimizations.
There is also context compression—specialized models can “compress” a long text into a concise representation that preserves the key information. It’s like taking notes: 100 pages of a lecture become 5 pages of the most important points.
Context windows of popular models
The world of language models is evolving rapidly, but it is worth knowing the current capabilities. The latest GPT-5 combines a large context window, which allows it to process several hundred thousand tokens at once, with high-quality reasoning and consistent responses. It is a model that handles the analysis of extensive materials, the design of complex solutions, and work on texts that previously required multiple stages of division.
Gemini 1.5 Pro, in turn, pushes the boundaries of sheer physical capacity: with over two million tokens, the model can process extensive document archives or several books simultaneously. Such a large context window is not always necessary, but it opens up new possibilities, especially in research and auditing, where being able to examine the entire body of material without fragmentation is crucial.
In the open-source segment, Meta is developing Llama 3.3, which offers up to 128,000 tokens and remains one of the most interesting options for those who value flexibility and control over their data. At the same time, lighter long-context models are being developed, such as new variants of Mistral, which, at a much lower cost, provide a genuine ability to work with extensive documents.
Although impressive figures are striking, it is worth remembering that the context window size is only one piece of the puzzle. The quality of working with long contexts is also determined by how the model uses the available information and how consistently it carries out its reasoning. Choosing the right model is therefore not simply a matter of selecting the maximum number of tokens, but of matching the tool to your actual needs.
Context Window in the Human Mind.
Anterograde Amnesia and the Interesting Case of H.M.
This is best illustrated by one of the most fascinating cases in the history of neuroscience: the story of Henry Molaison, known to the world as patient H.M. Although his brain had lost the ability to form new lasting memories, Henry remained a bright, courteous, and engaging conversationalist. He could solve crossword puzzles, crack jokes, and engage in logical discussions, as long as they fit within his several-minute “window of the present.” In a sense, modern artificial intelligence is a digital counterpart to this extraordinary state of mind. The model is brilliant and articulate, but like Henry, it lives in an eternal “now.” It does not have a “digital hippocampus” that would record your conversation on the hard drive of its personality. When the token limit is exhausted, the oldest threads simply dissolve into oblivion, and the AI continues the conversation with undiminished enthusiasm, focused solely on what it sees at that moment.
The context window also applies to every human being, although it is more difficult to notice.
The context window also applies to every human being, although this is not easy to notice at first glance. The brain processes information in cycles that include sensory memory, working memory, and early hippocampus-dependent consolidation. Each of these stages operates within a specific time frame, just as language models define the number of characters that can be taken into account at any given moment. In our case, this window consists of the set of information that the mind can retain, connect, and use at a given moment. When too many new stimuli appear, earlier traces become less accessible because the system must make room for additional elements.
The difficulty lies in the fact that these processes are not as clear-cut in humans as they are in an algorithm. Working memory has a limited capacity, and early consolidation, which lasts from several to several dozen minutes, is vulnerable to disruption. The hippocampus maintains fresh information before it is transferred into long-term storage, so if too many new signals appear during this time, the brain will partially overwrite the previous content. As a result, we remember not so much what we actually saw or heard as what fit within our internal context window and was consolidated in time.
Only when we realize that this limited window exists can we more easily understand why some conversations lose their thread, why it is difficult to recall a minor detail from fifteen minutes ago, or why intense situations so strongly displace earlier events from memory. This is not a flaw, but rather a natural feature of the human brain’s architecture, which operates similarly to AI models, although it is far more complex and delicate.
Learning new things and complex conceptual work.
In practice, this can be seen when learning new, demanding things, as well as during conceptual work. In such situations, it is helpful to quickly provide yourself with useful information—in other words, to create the right context. People therefore print out notes and stick them on the wall, browse short summaries, or return to earlier texts. Artists prepare inspiration boards, and in the design process, a mood board is created that serves the same function: it organizes the world of stimuli so that the mind can work with the right set of data.
With this context in mind, it is easier to perform a more complex task that is then easier to remember, or to create something concrete, such as a text or a program. Once the work is finished, there is no longer any need to keep many small pieces of information in memory, because they have been used and will no longer be relevant to subsequent activities.
A similar mechanism operates in the division of labor. When a team creates a complex project, no one is able to keep all the knowledge needed to carry it out in mind. Each person therefore works within their own fragment of context, appropriate to their tasks. The programmer works with technical requirements, the designer uses mood boards and wireframes, and the person responsible for strategy focuses on data and business assumptions. Each of them possesses only part of the complete picture, but that part is sufficient to make sound decisions and move the project forward. Shared artifacts, such as documents, diagrams, or brief summaries, allow the team to synchronize individual context windows so that everything works coherently without requiring every person to store everything in memory.
The “lost in the middle” problem.
What is the “lost in the middle” phenomenon?
Language models, despite their impressive technical capabilities, exhibit a specific weakness when processing long texts. This phenomenon, called “lost in the middle” by researchers, consists in systematically overlooking or inaccurately analyzing information placed in the middle sections of long sequences.
The mechanism works in a paradoxical way: technically, the model “sees” all the tokens in its context window, has access to them, and can theoretically process them equally effectively. In practice, however, the quality of understanding is not uniform. Key data hidden between the beginning and the end of a document may be practically ignored, even though mathematically they are fully within the attention range.
This is not an accidental imperfection; it is a systematic pattern that appears in all major language models tested in this regard. Regardless of the specific architecture or model size, the effect remains noticeable, and its severity increases proportionally to the length of the context.
Empirical Evidence for the Existence of the Problem
Research conducted by teams from leading AI laboratories has yielded alarming findings. In experiments where key information necessary to answer a question was placed at various points within a long context, models performed significantly worse when that information was located in the middle.
In one of the tests, researchers created documents containing approximately 20,000 tokens and hid a specific fact in them, such as a phone number or a meeting date. When this information appeared in the first 10% or the last 10% of the document, the models retrieved it with an accuracy of over 90%. However, when the same information was placed somewhere between 40% and 60% of the text’s length, performance dropped to as low as 30–40%.
Moreover, this problem intensifies in proportion to the context length. In sequences containing 4,000 tokens, the effect is noticeable but moderate. At 32,000 tokens, it already becomes a serious limitation. And when we talk about experimental models operating on 100,000 or 200,000 tokens, the middle part of the context may be practically “invisible” to the model.
Particularly revealing are tests with “needle in a haystack” documents, where one specific fact is hidden among a sea of irrelevant information. Models easily find the “needle” at the beginning or end of the “haystack,” but when it is located precisely in the middle, the success rate drops dramatically—sometimes by as much as 60–70 percentage points.
Why Do Models Lose Focus?
The causes of this phenomenon are multifaceted and stem from both the architecture of the models and the way they are trained.
Training bias plays a key role here. During training, models learn from millions of documents, but most natural texts have a specific structure: important information appears at the beginning (introduction, theses) and at the end (summary, conclusions). The middle is often filled with details, elaborations, and digressions. The model therefore learns a statistical pattern: “beginnings and endings are more important.” This pattern is reinforced by billions of examples and becomes difficult to overcome, even when a particular document has a different structure.
The limitations of the attention mechanism also play a role. Although, in theory, each token can “look at” every other token, in practice, attention weights must be normalized—they must sum to 1. With very long sequences, the “attention budget” is distributed across more and more tokens, causing individual fragments, even important ones, to receive increasingly smaller weights. Each token in a sequence of 100,000 elements receives, on average, just 0.001% of the total available attention.
Gradient degradation during backpropagation is another factor. During training, the error signal must “flow” backward through the entire network. The longer the sequence, the more difficult it is for the signal to reach the middle tokens with sufficient strength to correct errors. The model learns more effectively from tokens near the beginning and end of the sequence, where the signal is stronger.
The information dilution effect is an additional problem. In long sequences, the noise-to-signal ratio increases, and the model must sift through hundreds or thousands of tokens, most of which are less relevant. Although advanced, attention mechanisms cannot perfectly separate key information from filler, especially when it is distributed evenly throughout the text.
Practical implications for users
This phenomenon has real implications for anyone working with large language models. If you paste a lengthy document into a model and ask a question about information located somewhere in the middle, you risk receiving an imprecise answer or having a crucial passage omitted entirely.
For analysts processing lengthy reports, this means that prompts must be structured deliberately. Placing the most important information at the beginning or end of the context increases the likelihood that the model will notice it. Some users apply the “sandwich” technique—repeating key passages both at the beginning and at the end of a long text.
In applications such as document summarization or information extraction, the problem is even more apparent. The model may create a summary containing elements from the first and last chapters while completely omitting important threads from the middle sections. To someone unfamiliar with the original document, such a summary may appear complete, while in reality it lacks critical elements.
In the context of legal or medical analysis, the consequences are even more serious. Omitting a key provision from the middle of a contract or an important symptom from the middle section of medical records can lead to incorrect conclusions with potentially serious consequences. Professionals using AI must be aware of this risk and not rely blindly on analyses generated by the model.
Are there solutions?
The research community is working intensively to mitigate this problem, although a universal solution remains elusive.
One approach is training with distributed information—deliberately placing key data in different locations throughout long sequences during training, with particular emphasis on the middle sections. However, this requires redesigning entire training datasets and drastically increasing computational costs. Some newer models, such as Claude 3 and GPT-4, show improvement in this area, although the problem has not been completely eliminated.
Hierarchical processing is a different approach. Instead of processing the entire text at once, the model first divides it into segments, processes each one locally, and then combines the results at a higher level of abstraction. This helps avoid situations where middle sections get lost in a sea of tokens, but introduces new challenges related to the loss of context between segments.
Sliding window attention mechanisms limit the scope of attention to a local neighborhood, which reduces the attention dilution effect, albeit at the cost of capturing long-range dependencies. Models such as Mistral use this technique with some success.
The most promising approaches appear to be new attention architectures, such as Sparse Attention, Longformer, and solutions based on State Space Models (SSMs). They intelligently select which tokens should “communicate” with one another instead of trying to compare every token with every other. However, this is still an active area of research, and implementing these solutions in production systems requires time and significant resources.
How to deal with the problem today?
Until better models emerge, users can apply practical strategies to minimize the impact of the “lost in the middle” phenomenon.
A Well-Thought-Out Prompt Structure is essential. Place the most important instructions and context at the beginning and end of the prompt. If you need to provide a long document, add a brief reminder of the key points at the end or a direct question referring to specific sections.
Splitting tasks into smaller parts often produces better results than trying to process everything at once. Instead of asking the model to analyze a 50-page document in a single prompt, you can divide it into chapters and process them sequentially, then synthesize the results.
The map-reduce technique works well for very long texts: first, you process each fragment separately (map), and then combine the results into a coherent whole (reduce). This adds an intermediate step, but dramatically improves the quality of processing for the middle sections of the document.
Clear instructions in the prompt can help the model. Phrases such as “pay particular attention to the middle section of the document” or “key information can be found in paragraphs 15–20” increase the chances that the model will allocate its attention appropriately. You can also use markers such as “[IMPORTANT]” or “[KEY INFORMATION]” for critical passages.
Iterative questioning is another effective method. Instead of asking one broad question about the entire document, ask a series of more precise questions referring to specific sections. This forces the model to focus its attention on particular passages rather than trying to process everything at once.
Understanding the “lost in the middle” phenomenon is key to working effectively with large language models. This does not mean that long contexts are useless—quite the contrary, they remain extremely valuable. However, it is important to remember their limitations and consciously design interactions with the model to make the most of its capabilities while minimizing its weaknesses.
How Researchers Are Working to Expand the Context Window.
The race toward ever-larger context windows continues, but it is not the only path for development. Researchers are working on models with variable attention resolution, capable of automatically deciding which parts of the context require detailed analysis and which can be treated superficially.
Another promising approach is long-term memory: models that can store and retrieve information from previous sessions, much as people remember earlier conversations. However, this would require a fundamental change in the architecture. This is not simply a matter of adding RAG and an ordinary “additional memory” mechanism. ChatGPT has had a multi-threaded memory function for a long time. As I wrote earlier, RAG can solve some of the problems arising from the limitations of the context window, but it is not capable of fixing everything. In this >>context<<, RAG is simply a provisional solution.
The future probably belongs to hybrid solutions: models with reasonably large context windows (10,000–50,000 tokens) supported by intelligent search and external memory systems. Not every problem requires a million-token window; sometimes a smarter way of managing information is enough.
One thing is certain: context window limitations are a fundamental problem that defines the capabilities and constraints of modern AI. Understanding this helps us design systems better and avoid expecting the impossible from models. Because sometimes less is more, if you know how to use it.